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SBMI-LTD: stationary background model initialization based on low-rank tensor decomposition

Authors:

Thierry Bouwmans,

Soon Ki JungAuthors Info & Claims

SAC '17: Proceedings of the Symposium on Applied Computing

Pages 195 - 200

https://doi.org/10.1145/3019612.3019687

Published: 03 April 2017 Publication History

Abstract

Initialization of background model also known as foreground-free image against outliers or noise is a very important task for various computer vision applications. Tensor deomposition using Higher Order Robust Principal Component Analysis has been shown to be a very efficient framework for exact recovery of low-rank (corresponds to the background model) component. Recent study shows that tensor decomposition based on online optimization into low- rank and sparse component addressed the limitations of memory and computational issues as compared to the earlier approaches. However, it is based on the iterative optimization of nuclear norm which is not always robust when the large entries of an input observation tensor are corrupted against outliers. Therefore, the task of background modeling shows a weak performance in the presence of an increasing number of outliers. To address this issue, this paper presents an extension of an online tensor decomposition into low-rank and sparse components using a maximum norm constraint. Since, maximum norm regularizer is more robust than nuclear norm against large number of outliers, therefore the proposed extended tensor based decomposition framework with maximum norm provides an accurate estimation of background scene. Experimental evaluations on synthetic data as well as real dataset such as Scene Background Modeling Initialization (SBMI) show encouraging performance for the task of background modeling as compared to the state of the art approaches.

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Cited By

Wang JHuang TZhao XLuo YJiang T(2024)CoNoT: Coupled Nonlinear Transform-Based Low-Rank Tensor Representation for Multidimensional Image CompletionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.321719835:7(8969-8983)Online publication date: Jul-2024
https://doi.org/10.1109/TNNLS.2022.3217198
Cao XZhang XZhu CLiu JLiu Y(2024)TS-RTPM-Net: Data-Driven Tensor Sketching for Efficient CP DecompositionIEEE Transactions on Big Data10.1109/TBDATA.2023.331025410:1(1-11)Online publication date: Feb-2024
https://doi.org/10.1109/TBDATA.2023.3310254
Feng LZhu CLong ZLiu JLiu Y(2023)Multiplex Transformed Tensor Decomposition for Multidimensional Image RecoveryIEEE Transactions on Image Processing10.1109/TIP.2023.328467332(3397-3412)Online publication date: 2023
https://doi.org/10.1109/TIP.2023.3284673
Show More Cited By

Index Terms

SBMI-LTD: stationary background model initialization based on low-rank tensor decomposition
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Video segmentation

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Published In

cover image ACM Conferences

SAC '17: Proceedings of the Symposium on Applied Computing

April 2017

2004 pages

ISBN:9781450344869

DOI:10.1145/3019612

Conference Chair:
Sung Y. Shin
South Dakota State University
,
Program Chairs:
Dongwan Shin
New Mexico Tech
,
Maria Lencastre
University of Pernambuco, Brazil

Copyright © 2017 ACM.

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

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Published: 03 April 2017

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Korean Government

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SAC 2017

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SIGAPP

SAC 2017: Symposium on Applied Computing

April 3 - 7, 2017

Marrakech, Morocco

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

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Cited By

Wang JHuang TZhao XLuo YJiang T(2024)CoNoT: Coupled Nonlinear Transform-Based Low-Rank Tensor Representation for Multidimensional Image CompletionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.321719835:7(8969-8983)Online publication date: Jul-2024
https://doi.org/10.1109/TNNLS.2022.3217198
Cao XZhang XZhu CLiu JLiu Y(2024)TS-RTPM-Net: Data-Driven Tensor Sketching for Efficient CP DecompositionIEEE Transactions on Big Data10.1109/TBDATA.2023.331025410:1(1-11)Online publication date: Feb-2024
https://doi.org/10.1109/TBDATA.2023.3310254
Feng LZhu CLong ZLiu JLiu Y(2023)Multiplex Transformed Tensor Decomposition for Multidimensional Image RecoveryIEEE Transactions on Image Processing10.1109/TIP.2023.328467332(3397-3412)Online publication date: 2023
https://doi.org/10.1109/TIP.2023.3284673
Wu JPu WAn HHuang YYang HYang J(2023)Learning-Based High-Frame-Rate SAR ImagingIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2023.327969461(1-13)Online publication date: 2023
https://doi.org/10.1109/TGRS.2023.3279694
Wu T(2023)Online Tensor Low-Rank Representation for Streaming Data ClusteringIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2022.320748433:2(602-617)Online publication date: Feb-2023
https://doi.org/10.1109/TCSVT.2022.3207484
Wang JHuang TZhao XMiao Y(2022)Complex Video Completion Fusing Low-Rank Background and Deep Foreground PriorsIEEE Signal Processing Letters10.1109/LSP.2023.323658529(2737-2741)Online publication date: 2022
https://doi.org/10.1109/LSP.2023.3236585
Shakeri MZhang H(2019)Moving Object Detection Under Discontinuous Change in Illumination Using Tensor Low-Rank and Invariant Sparse Decomposition2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR.2019.00739(7214-7223)Online publication date: Jun-2019
https://doi.org/10.1109/CVPR.2019.00739
Kalsotra RArora S(2019)A Comprehensive Survey of Video Datasets for Background SubtractionIEEE Access10.1109/ACCESS.2019.29149617(59143-59171)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2914961
Bouwmans TJaved SSultana MJung S(2019)Deep neural network concepts for background subtraction: A systematic review and comparative evaluationNeural Networks10.1016/j.neunet.2019.04.024Online publication date: May-2019
https://doi.org/10.1016/j.neunet.2019.04.024
Bouwmans TJaved SZhang HLin ZOtazo R(2018)On the Applications of Robust PCA in Image and Video ProcessingProceedings of the IEEE10.1109/JPROC.2018.2853589106:8(1427-1457)Online publication date: Aug-2018
https://doi.org/10.1109/JPROC.2018.2853589

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